Third-order cumulants based methods for continuous-time errors-in-variables model identification

Statistical Analysis of a Third-Order Cumulants Based Algorithm for Discrete-Time Errors-In-Variables Identification

This paper deals with identification of dynamic discrete-time errors-in-variables systems. The statistical accuracy of a least squares estimator based on third-order cumulants is analyzed. In particular, the asymptotic covariance matrix of the estimated parameters is derived. The results are supported by numerical simulation studies.

Cumulant based identification approaches for nonminimum phase FIR systems

Recursive and least squares methods for identification of non-minimum-phase linear time-invariant (NMP-LTI) FIR systems are developed. The methods utilize the secondand third-order cumulants of the output of the FIR system whose input is an independent, identically distributed (i.i.d.) non-Gaussian process. Since knowledge of the system order is of utmost importance to many system identificatio...

Extension of Linear Channels Identification Algorithms to Non Linear Using Selected Order Cumulants

In this paper, we present an extension of linear communication channels identification algorithms to non linear channels using higher order cumulants (HOC). In the one hand, we develop a theoretical analysis of non linear quadratic systems using second and third order cumulants. In the other hand, the relationship linking cumulants and the coefficients of non linear channels presented in the li...

The CONTSID toolbox for Matlab: extensions and latest developments

This paper describes the latest developments for the CONtinuous-Time System IDentification (CONTSID) toolbox to be run with Matlab which includes time-domain identification methods for estimating continuous-time models directly from sampled data. The main additions to the new version aim at extending the available methods to handle wider practical situations in order to enhance the application ...

A cumulant statistics-based method for continuous-time errors-in-variables model identification